Saliency maps [1]} provide further insights into the interpretability of the models, which highlight the parts of the signal that influence the most in making the predictions. Saliency maps can be generated by computing the gradient of the loss function with respect to the individual timestamps of the input signal [2]}. For example, for an \(n\)  sample long input signal, \(X=[x_1,x_2,x_3,\dots ,x_n]\) , we can compute the saliency map, \(S=[s_1,s_2,s_3,\dots ,s_n]\)  as \(s_i=|\frac{\delta L}{\delta x_i}|,\forall i \epsilon [1 \dots n]\) , where \(L\)  is the value of the loss function.
